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3D Human Pose Estimation with Grouping Regression 1

14th Conference on Image and Graphics Technologies and Applications, IGTA 2019

作者： He, Xuesheng Wang, Huabin Qin, Yuan Tao, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230031 China

ISBN: (数字)9789811399176

ISBN: (纸本)9789811399169

Most of the methods for predicting the 3D human pose from single picture are to first extract the 2d joint position in the image, and then use the 2d joint coordinates to get the 3d joint position. This type of method focuses on learning the mapping from 2d to 3d, and neglects the kinematic properties of the joints. Combined with the independent characteristics of human limb joint motion, we adopt the structure of grouping regression. The limbs and trunk were divided into different joint groups, and the 3d position of the joints in each group was predicted separately. In order to make the prediction results are closer to the real human posture, we use BiLSTM to design a human joint self-constrained network to adjust prediction results and improve accuracy. Experiments show that the method of grouping regression combined with self-constrained network is superior to the current advanced method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Wideband 10-port mimo antenna array for 5g metal-frame smartphone applications

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Progress In Electromagnetics Research C 2020年 104卷 229-240页

作者： Liu, Peng Sun, Yufa Liu, Tao Li, Qing Wang, Xuefeng Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

A wideband 10-port multiple input multiple output (MIMO) antenna array operated below 6 GHz for the fifth generation (5G) metal-frame smartphones is presented and discussed in this paper. The proposed MIMO antenna array element is composed of a microstrip line with a tuning stub and rectangular slot. The size of the rectangular slot is only 15 mm × 2 mm (0.211λ0 × 0.028λ0, and λ0 is the wavelength with the resonance frequency of 4.2 GHz). U-shaped slots on the substrate are used to reduce the mutual coupling between the antenna elements. At the same time, in order to improve the radiation characteristics of the antenna arrays, narrow slits are etched in the metal-frame. The proposed antenna covers 3.3–5.5 GHz (S11 © 2020, Electromagnetics Academy. All rights reserved.

关键词： Antenna arrays

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A New User Grouping and Power Allocation Scheme for Downlink Non-orthogonal Multiple Access Systems 3

A New User Grouping and Power Allocation Scheme for Downlink...

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2019 3rd International Conference on Data Mining, Communications and Information Technology, DMCIT 2019

作者： Wang, Yin Li, Xiaohui Wang, Wenwu Zhang, Gongquan Zhang, Hongwei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

In this paper, we propose a new user grouping and power allocation scheme based on beamforming for downlink non-orthogonal multiple access systems. The proposed user grouping scheme can effectively reduce the interference from the other user and other beams as well, and can effectively improve the weak user rate especially when the SNR is not large. In addition, a power allocation scheme that can maximize the sum capacity while satisfying a certain fairness index is proposed. From the simulation results, the user grouping and power allocation method proposed in this paper can not only improve the overall system throughput performance, but also improve the fairness of weak users. © 2019 Published under licence by IOP Publishing Ltd.

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Multi-Attention Network for 2D Face Alignment in the Wild 14th

Multi-Attention Network for 2D Face Alignment in the Wild

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14th Conference on Image and Graphics Technologies and Applications, IGTA 2019

作者： Liu, Xin Wang, Huabin Cheng, Rui Yan, Xiang Tao, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230031 China

ISBN: (纸本)9789811399169

Most existing algorithms based on Convolutional Neural Networks (CNNs) for face alignment ignore the significance of attention mechanism. In this paper, we propose a Multi-Attention Network (MANet) for robust face alignment. Our attention mechanism includes multi-level feature attention and multi-scale attention. Multi-level feature attention is introduced for the purpose of paying attention to features of different levels, specifically, high-level feature attentions are essential for correlations among neighboring regions whereas low-level feature attentions focus on detailed description for local parts. While multi-scale attention is designed to obtain better representation the features of different scales. The attentions mentioned above are utilized for better feature presentation and information flow, thus our network is guided to emphasize the key information and suppress the less significant information. The experimental results on 300 W and WFLW datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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DRSM: Efficient Neural 4D Decomposition for Dynamic Reconstruction in Stationary Monocular Cameras

DRSM: Efficient Neural 4D Decomposition for Dynamic Reconstr...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weixing Xie Xiao Dong Yong Yang Qiqin Lin Jingze Chen Junfeng Yao Xiaohu Guo Center for Digital Media Computing School of Film School of Informatics Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Department of Computer Science BNU-HKBU United International College Department of Computer Science The University of Texas at Dallas

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation.

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RCS Simulation and ISAR Imaging of Coated Targets 5

RCS Simulation and ISAR Imaging of Coated Targets

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5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Tan, Xiaolong Fang, Ming Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781538671115

The application of radar absorbing materials on the surface of metal aircraft can effectively reduce the radar cross section (RCS). In this paper, the physical optics (PO) method is used to calculate the RCS of electrically large coated targets. First, we calculate the RCS of the original X29 aircraft and the X29 aircraft coated with a broadband two-layer radar absorbing coating. In addition, the backscattered electric field data of coated and uncoated X29 aircraft are obtained by using the PO method, respectively. Finally, the two-dimensional Inverse Fourier Transform (IFT) of the collected data is taken to form the Inverse Synthetic Aperture Radar (ISAR) image. © 2019 IEEE.

关键词： Radar cross section

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ICR-Net: Semi-Supervised Medical Image Segmentation Guided By Intra-Sample Cross Reconstruction

ICR-Net: Semi-Supervised Medical Image Segmentation Guided B...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xianpeng Cao Weixing Xie Xianxing Cao Qiqin Lin Rongzhou Zhou Junfeng Yao Qingqi Hong Center for Digital Media Computing School of Film Xiamen University Xiamen China National Institute for Data Science in Health and MedicineXiamen University College of Computer and Cyber Security Fujian Normal University Fuzhou China School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Semi-supervised learning is becoming increasingly popular in medical image segmentation because of its ability to exploit large amounts of unlabelled data to extract additional information. However, most existing semi-supervised segmentation methods focus only on extracting information from unlabelled data, ignoring the potential of labelled data to further improve model performance. In this paper, we propose a new framework for Intra-Sample Cross Reconstruction Networks (ICR-Net) that utilises labelled data to help the network extract information from unlabelled data, thereby guiding the network’s regularisation learning. Our method contains two modules: Intra-Sample Cross Reconstruction (ICR) module and Synergistic Consistency Constraints (SCC) module. The ICR module processes the labelled data features in a more fine-grained manner, thus enabling the network to learn and capture the key patterns and features in the inputs more efficiently, and the SCC guides the network’s regularised learning by formulating additional model regularisations. Experiments on the LA dataset and the pancreas dataset show that our proposed framework is more effective than current state-of-the-art methods in medical image segmentation tasks.

关键词： Image segmentation Correlation Semisupervised learning Feature extraction Data models Data mining Pancreas Image reconstruction Biomedical imaging

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Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology Images

Multi-Object Localization and Irrelevant-Semantic Separation...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ya Tang Xiongjun Ye Xuanya Li Zhineng Chen Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Baidu Inc. Beijing China School of Computer Science Fudan University Shanghai China

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effective nuclei segmentation method for histopathology images based on a novel neural network for multi-object localization and irrelevant-semantic separation (MI-Net), which includes a multi-object localization module (MOLM), a deep boundary awareness module (DBAM), and an irrelevant semantic separation module (ISSM). Specifically, the MOLM is used to calibrate features to capture more comprehensive information about the location of nuclei. It alleviates the problem of cell adhesion in histopathology images. The DBAM is intended to extract boundary information and solve the blurred boundary problem effectively. The ISSM is used to separate foreground features from background features and effectively address the problem of complex backgrounds in histopathology images. It also solves the low contrast problem between the target objects and the background. We evaluate MI-Net on the famous MoNuSeg dataset and compare it with ten state-of-the-art methods. MI-Net surpasses the best-performed method by margins from 1.12% to 2.29% over standard evaluation metrics, showing its effectiveness on nuclei segmentation.

关键词： Location awareness Image segmentation Histopathology Semantics Neural networks signal processing Task analysis

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A Transformer-based Neural Architecture Search Method

arXiv

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arXiv 2025年

作者： Wang, Shang Tang, Huanrong Ouyang, Jianquan Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science School of Cyberspace Security Xiangtan University Hunan Xiangtan China

This paper presents a neural architecture search method based on Transformer architecture, searching cross multihead attention computation ways for different number of encoder and decoder combinations. In order to search for neural network structures with better translation results, we considered perplexity as an auxiliary evaluation metric for the algorithm in addition to BLEU scores and iteratively improved each individual neural network within the population by a multi-objective genetic algorithm. Experimental results show that the neural network structures searched by the algorithm outperform all the baseline models, and that the introduction of the auxiliary evaluation metric can find better models than considering only the BLEU score as an evaluation metric. © 2025, CC BY.

关键词： Genetic algorithms

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Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection

Automatic Segmentation of Nasopharyngeal Carcinoma in CT Ima...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qizhi Wang Wei Huang Yuan Zhang Xuanya Li Xiongjun Ye Kai Hu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Department of Radiology the First Hospital of Changsha Changsha China Baidu Inc. Beijing China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation particularly challenging. To address these problems, we propose an accurate and effective NPC segmentation method using Dual Attention and Edge Detection Convolutional Neural Network (DAED-Net). Firstly, we combine a 2.5D convolutional neural network with UNet++ and propose a new backbone called Dual-dimension Dense UNet (DD-UNet), which can extract more beneficial features from 3D images. Secondly, a Dual Attention Module (DAM) is proposed to help the model better segment the target region of NPC by efficiently collecting spatial and channel attention information from feature maps. Moreover, an Edge Detection Module (EDM) is introduced in the network to enhance the segmentation of the target contours. Finally, we evaluate the proposed DAED-Net on the public MICCAI 2019 StructSeg NPC dataset from different perspectives. Numerical and visual results show that the proposed method outperforms nine state-of-the-art segmentation methods and yields more accurate NPC segmentation results.

关键词： Image segmentation Visualization Three-dimensional displays Dams Computed tomography Image edge detection Feature extraction

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